Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/agenticnotetaking/arscontexta/reflectnpx skills add agenticnotetaking/arscontexta --skill reflectgit clone --depth 1 https://github.com/agenticnotetaking/arscontextaWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00062 | $0.06589 |
| Opus 5 | $0.00031 | $0.03295 |
| Sonnet 5 | $0.00012 | $0.01318 |
| Haiku 4.5 | $0.00006 | $0.00659 |
Grade C, and why
reflect scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf "$LOCKDIR" How it starts
The opening of the file, as written. The whole thing — 747 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Runtime Configuration (Step 0 — before any processing)
Read these files to configure domain-specific behavior:
-
ops/derivation-manifest.md— vocabulary mapping, platform hints- Use
vocabulary.notesfor the notes folder name - Use
vocabulary.note/vocabulary.note_pluralfor note type references - Use
vocabulary.reflectfor the process verb in output - Use
vocabulary.topic_map/vocabulary.topic_map_pluralfor MOC references - Use
vocabulary.cmd_reweavefor the next-phase suggestion - Use
vocabulary.inboxfor the inbox folder name
- Use
-
ops/config.yaml— processing depth, pipeline chainingprocessing.depth: deep | standard | quickprocessing.chaining: manual | suggested | automatic
If these files don't exist, use universal defaults.
Processing depth adaptation:
| Depth | Connection Behavior |
|---|---|
| deep | Full dual discovery (MOC + semantic search). Evaluate every candidate. Multiple passes. Synthesis opportunity detection. Bidirectional link evaluation for all connections. |
| standard | Dual discovery with top 5-10 candidates. Standard evaluation. Bidirectional check for strong connections only. |
| quick | Single pass — either MOC or semantic search. Accept obvious connections only. Skip synthesis detection. |
EXECUTE NOW
Target: $ARGUMENTS
Parse immediately:
- If target contains
[[note name]]or note name: find connections for that {vocabulary.note} - If target contains
--handoff: output RALPH HANDOFF block at end - If target is empty: check for recently created {vocabulary.note_plural} or ask which {vocabulary.note}
- If target is "recent" or "new": find connections for all {vocabulary.note_plural} created today
Execute these steps:
- Read the target {vocabulary.note} fully — understand its claim and context
- Throughout discovery: Capture which {vocabulary.topic_map_plural} you read, which queries you ran (with scores), which candidates you evaluated. This becomes the Discovery Trace — proving methodology was followed, not reconstructed.
- Run Phase 0 (index freshness check)
- Use dual discovery in parallel:
- Browse relevant {vocabulary.topic_map}(s) for related {vocabulary.note_plural}
- Run semantic search for conceptually related {vocabulary.note_plural}
- Evaluate each candidate: does a genuine connection exist? Can you articulate WHY?
- Add inline wiki-links where connections pass the articulation test
- Update relevant {vocabulary.topic_map}(s) with this {vocabulary.note}
- If task file in context: update the {vocabulary.reflect} section
- Report what was connected and why
- If
--handoffin target: output RALPH HANDOFF block
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 747 lines · 62 tokens per session scan C 98fef2d260f4
reflect is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,486 stars, last pushed 6mo ago), licensed MIT. It adds 62 tokens to every session and 6,589 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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